The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective
Open accessAbstract
Smart interactions among the smart grid, aggregators, and EVs can bring various benefits to all parties involved, e.g., improved reliability and safety for the smart gird, increased profits for the aggregators, as well as enhanced self benefit for EV customers. This survey focuses on viewing this smart interactions from an algorithmic perspective. In particular, important dominating factors for coordinated charging from three different perspectives are studied, in terms of smart grid oriented, aggregator-oriented, and customer-oriented smart charging. Firstly, for smart grid oriented EV charging, we summarize various formulations proposed for load flattening, frequency regulation, and voltage regulation, then explore the nature and substantial similarity among them. Secondly, for aggregator-oriented EV charging, we categorize the algorithmic approaches proposed by research works sharing this perspective as direct and indirect coordinated control, and investigate these approaches in detail. Thirdly, for customer-oriented EV charging, based on a commonly shared objective of reducing charging cost, we generalize different formulations proposed by studied research works. Moreover, various uncertainty issues, e.g., EV fleet uncertainty, electricity price uncertainty, regulation demand uncertainty, etc., have been discussed according to the three perspectives classified. At last, we discuss challenging issues that are commonly confronted during modeling the smart interactions, and outline some future research topics in this exciting area.
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Cites
Yilmaz, M., & Krein, P. T. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2012.2212917 · Cited by 3,118
Kempton, W., & Tomić, J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.12.022 · Cited by 2,126
Probability and Random Processes.
Grimmett, G. R., & Stirzaker, D. R. (2001) · doi:10.1093/oso/9780198572237.001.0001 · Cited by 1,850
Electric Vehicle Technology Explained
Larminie, J., & Lowry, J. (2012) · doi:10.1002/9781118361146 · Cited by 1,384
Mwasilu, F., Justo, J. J., Kim, E.-K., Do, T. D., & Jung, J.-W. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.03.031 · Cited by 1,162
Synthesize battery degradation modes via a diagnostic and prognostic model
Dubarry, M., Truchot, C., & Liaw, B. Y. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.07.016 · Cited by 1,065
Han, X., Ouyang, M., Lu, L., Li, J., Zheng, Y., & Li, Z. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.11.029 · Cited by 822 · cited 1×
“different types of Li-ion batteries have different aging characteristics, thus need different aging mechanisms [40], [46].”
BackgroundFlexible charging optimization for electric vehicles considering distribution grid constraints
Sundstrom, O., & Binding, C. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2011.2168431 · Cited by 571
Modeling Lithium Ion battery degradation in electric vehicles
Millner, A. (2010) · 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply · doi:10.1109/citres.2010.5619782 · Cited by 397
Cheng, H. T., Shan, H., & Zhuang, W. (2011) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2010.11.009 · Cited by 273
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Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
Hassan, M. U., Rehmani, M. H., & Chen, J. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.060 · Cited by 520
Reinforcement Learning for Real-Time Pricing and Scheduling Control in EV Charging Stations
Wang, S., Bi, S., & Zhang, Y. A. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2950809 · Cited by 281
A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid
Nimalsiri, N. I., Mediwaththe, C. P., Ratnam, E. L., Shaw, M., Smith, D. B., & Halgamuge, S. K. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2943620 · Cited by 271
Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
Lee, Z. J., Lee, G., Lee, T., Jin, C., Lee, R., Low, Z., Chang, D., Ortega, C., & Low, S. H. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3074437 · Cited by 174
“There is also a tremendous literature on EV charging algorithms; see surveys [9], [10] for extensive pointers to the literature.”
Background
Metadata
- Authors
- Wang, Q., Liu, X., Du, J., & Kong, F.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2016
- Volume
- 18
- Issue
- 2
- Pages
- 1500-1517
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2345271292
- Cited by
- 273
- References
- 12
- FWCI
- 11.28
- Type
- journal article
- Language
- en
- Open access
- green
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- Wang, Q., Liu, X., Du, J., & Kong, F. (2016). Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2016.2518628
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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